{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 第二周作业\n",
    "\n",
    "#### 问题描述\n",
    "请在Pima Indians Diabetes Data Set（皮马印第安人糖尿病数据集）进行分类器练习。\n",
    "\n",
    "#### 批改标准\n",
    "需要提交代码文件，并给出必要的结果解释。\n",
    "\n",
    "1) 训练数据和测试数据分割（随机选择20%的数据作为测试集）；（10分）\n",
    "\n",
    "2) 适当的特征工程（及数据探索）;（10分）\n",
    "\n",
    "3) Logistic回归，并选择最佳的正则函数（L1/L2）及正则参数；（30分）\n",
    "\n",
    "4) 线性SVM，并选择最佳正则参数，比较与Logistic回归的性能，简单说明原因。（20分）\n",
    "\n",
    "5) RBF核的SVM，并选择最佳的超参数（正则参数、RBF核函数宽度）；（30分）\n",
    "\n",
    "#### 字段说明\n",
    "\n",
    "Pregnancies: 怀孕次数\n",
    "\n",
    "Glucose: 口服葡萄糖耐受试验中,2 小时的血浆葡萄糖浓度。\n",
    "\n",
    "BloodPressure: 舒张压(mm Hg)\n",
    "\n",
    "SkinThickness: 三头肌皮肤褶层厚度(mm)\n",
    "\n",
    "Insulin:2 小时血清胰岛素含量(μU/ ml)\n",
    "\n",
    "BMI: 体重指数(体重,kg /(身高,m)^ 2)\n",
    "\n",
    "DiabetesPedigreeFunction: 糖尿病家族史\n",
    "\n",
    "Age: 年龄(岁)\n",
    "\n",
    "Outcome: 输出变了/类别标签(0 或 1,出现糖尿病为 1, 否则为 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入常用工具\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 导入可视化组件\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 参数设置\n",
    "params = {\n",
    "              'figure.figsize': (30, 10)\n",
    "              }\n",
    "\n",
    "sn.set_style('whitegrid')\n",
    "sn.set_context('talk')\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "pd.options.display.max_colwidth = 600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读入数据\n",
    "data = pd.read_csv('diabetes.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 各特征统计分布\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "没有缺失值, 但是Glucose、BloodPressure、SkinThickness、Insulin、BMI等特征最小值为0，医学上来讲应该不存在为0的情况，这里当做缺失值处理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Glucose            5\n",
      "BloodPressure     35\n",
      "SkinThickness    227\n",
      "Insulin          374\n",
      "BMI               11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 查看各个特征0值的个数\n",
    "empty_col_names = ['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']\n",
    "print((data[empty_col_names]==0).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SkinThickness、Insulin缺失率较高，其他三个特征缺失很少。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来看一下各个特征跟结果的相关度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb2f0a87a58>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 特征热度图\n",
    "corrmatt = data[['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome']].corr()\n",
    "mask = np.array(corrmatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sn.heatmap(corrmatt, mask=mask, vmax=1, square=True, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到SkinThickness、Insulin跟结果的相关度并不高，数据缺失又多，我们考虑把这两个特征抛弃。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure   BMI  DiabetesPedigreeFunction  Age\n",
       "0            6      148             72  33.6                     0.627   50\n",
       "1            1       85             66  26.6                     0.351   31\n",
       "2            8      183             64  23.3                     0.672   32\n",
       "3            1       89             66  28.1                     0.167   21\n",
       "4            0      137             40  43.1                     2.288   33"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分离特征和标签 抛弃空值较多的特征\n",
    "y_data = data['Outcome'].values\n",
    "x_data = data.drop(['Outcome', 'SkinThickness', 'Insulin'], axis=1)\n",
    "x_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                  0\n",
       "Glucose                      5\n",
       "BloodPressure               35\n",
       "BMI                         11\n",
       "DiabetesPedigreeFunction     0\n",
       "Age                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将为0的值替换为空\n",
    "empty_cols = ['Glucose', 'BloodPressure', 'BMI']\n",
    "x_data[empty_cols] = x_data[empty_cols].replace(0, np.nan)\n",
    "(x_data.isnull()).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用中值填补缺失值\n",
    "medians = x_data.median()\n",
    "x_data = x_data.fillna(medians)\n",
    "(x_data.isnull()).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/Swing/.local/lib/python3.7/site-packages/sklearn/preprocessing/data.py:617: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/home/Swing/.local/lib/python3.7/site-packages/sklearn/base.py:462: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.63994726,  0.86604475, -0.03198993,  0.16661938,  0.46849198,\n",
       "         1.4259954 ],\n",
       "       [-0.84488505, -1.20506583, -0.5283186 , -0.85219976, -0.36506078,\n",
       "        -0.19067191],\n",
       "       [ 1.23388019,  2.01666174, -0.69376149, -1.33250021,  0.60439732,\n",
       "        -0.10558415],\n",
       "       ...,\n",
       "       [ 0.3429808 , -0.02157407, -0.03198993, -0.910418  , -0.68519336,\n",
       "        -0.27575966],\n",
       "       [-0.84488505,  0.14279979, -1.02464727, -0.34279019, -0.37110101,\n",
       "         1.17073215],\n",
       "       [-0.84488505, -0.94206766, -0.19743282, -0.29912651, -0.47378505,\n",
       "        -0.87137393]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "ss_x = StandardScaler()\n",
    "x_data = ss_x.fit_transform(x_data)\n",
    "x_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 6)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 拆分测试集训练集 取20%作为测试机 其作为训练集\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, random_state=33, test_size=0.2)\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='liblinear',\n",
       "          tol=0.0001, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 采用网络搜索选择最好的正则函数和正则参数\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "penaltys = ['l1', 'l2']\n",
    "cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "params = dict(penalty=penaltys, C=cs)\n",
    "\n",
    "# 因为数据量较小，这里选择liblinear作分类器\n",
    "logisticReg = LogisticRegression(solver='liblinear')\n",
    "# 用log_loss作为评价指标\n",
    "grid = GridSearchCV(logisticReg, params, cv=5, scoring='neg_log_loss', return_train_score=True)\n",
    "grid.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00113449, 0.00125327, 0.00104537, 0.00126228, 0.00137253,\n",
       "        0.00136714, 0.00145559, 0.0014564 , 0.00151005, 0.00143876,\n",
       "        0.00144129, 0.00139613, 0.00144987, 0.00139756]),\n",
       " 'std_fit_time': array([3.56184546e-04, 7.98820515e-05, 6.47941869e-05, 3.62134513e-05,\n",
       "        9.56464976e-05, 6.87094714e-05, 4.71549278e-05, 4.36165532e-05,\n",
       "        8.13499565e-05, 6.32377765e-05, 4.36657363e-05, 3.32127949e-05,\n",
       "        7.05866218e-05, 4.52172385e-05]),\n",
       " 'mean_score_time': array([0.00141344, 0.00132122, 0.00135751, 0.0013001 , 0.00130668,\n",
       "        0.00135436, 0.00132375, 0.00130229, 0.00131483, 0.00130038,\n",
       "        0.00124922, 0.00128965, 0.00125608, 0.0012701 ]),\n",
       " 'std_score_time': array([1.98863197e-04, 3.51958155e-05, 7.83806872e-05, 3.47574713e-05,\n",
       "        2.87598829e-05, 6.85189611e-05, 5.78650094e-05, 1.93852926e-05,\n",
       "        3.82696801e-05, 3.68195486e-05, 9.40421527e-06, 3.69403872e-05,\n",
       "        2.50121885e-05, 3.56964131e-05]),\n",
       " 'param_C': masked_array(data=[0.001, 0.001, 0.01, 0.01, 0.1, 0.1, 1, 1, 10, 10, 100,\n",
       "                    100, 1000, 1000],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_penalty': masked_array(data=['l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1',\n",
       "                    'l2', 'l1', 'l2', 'l1', 'l2'],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'C': 0.001, 'penalty': 'l1'},\n",
       "  {'C': 0.001, 'penalty': 'l2'},\n",
       "  {'C': 0.01, 'penalty': 'l1'},\n",
       "  {'C': 0.01, 'penalty': 'l2'},\n",
       "  {'C': 0.1, 'penalty': 'l1'},\n",
       "  {'C': 0.1, 'penalty': 'l2'},\n",
       "  {'C': 1, 'penalty': 'l1'},\n",
       "  {'C': 1, 'penalty': 'l2'},\n",
       "  {'C': 10, 'penalty': 'l1'},\n",
       "  {'C': 10, 'penalty': 'l2'},\n",
       "  {'C': 100, 'penalty': 'l1'},\n",
       "  {'C': 100, 'penalty': 'l2'},\n",
       "  {'C': 1000, 'penalty': 'l1'},\n",
       "  {'C': 1000, 'penalty': 'l2'}],\n",
       " 'split0_test_score': array([-0.69314718, -0.638081  , -0.66047829, -0.51384078, -0.47684129,\n",
       "        -0.46268576, -0.46222192, -0.4606783 , -0.46100489, -0.4608581 ,\n",
       "        -0.4608995 , -0.46088257, -0.46088232, -0.46088509]),\n",
       " 'split1_test_score': array([-0.69314718, -0.64144136, -0.66089341, -0.52804237, -0.47486826,\n",
       "        -0.47680663, -0.47158221, -0.47152456, -0.47114655, -0.47115245,\n",
       "        -0.47112394, -0.47111809, -0.47111615, -0.47111468]),\n",
       " 'split2_test_score': array([-0.69314718, -0.64068098, -0.65903665, -0.53053184, -0.50878022,\n",
       "        -0.51072112, -0.52455011, -0.52490349, -0.52756975, -0.52760787,\n",
       "        -0.52788316, -0.52790014, -0.52792214, -0.5279296 ]),\n",
       " 'split3_test_score': array([-0.69314718, -0.63338526, -0.66412562, -0.49270221, -0.43729362,\n",
       "        -0.42566926, -0.42034382, -0.41957621, -0.41932148, -0.41925177,\n",
       "        -0.41923296, -0.41922444, -0.41922173, -0.41922176]),\n",
       " 'split4_test_score': array([-0.69314718, -0.64038895, -0.66076205, -0.52418576, -0.47544475,\n",
       "        -0.47465835, -0.47255099, -0.47315549, -0.47341166, -0.47348471,\n",
       "        -0.47351502, -0.47352616, -0.47352632, -0.4735304 ]),\n",
       " 'mean_test_score': array([-0.69314718, -0.63880056, -0.66105375, -0.51788472, -0.47470873,\n",
       "        -0.4701611 , -0.47031427, -0.47002936, -0.470554  , -0.47053383,\n",
       "        -0.47059391, -0.47059325, -0.47059671, -0.47059928]),\n",
       " 'std_test_score': array([0.        , 0.00292201, 0.00166703, 0.01378072, 0.0226216 ,\n",
       "        0.02733195, 0.03316565, 0.03360136, 0.03453782, 0.03458045,\n",
       "        0.03467513, 0.03468438, 0.03469248, 0.03469484]),\n",
       " 'rank_test_score': array([14, 12, 13, 11, 10,  2,  3,  1,  5,  4,  7,  6,  8,  9],\n",
       "       dtype=int32),\n",
       " 'split0_train_score': array([-0.69314718, -0.63882326, -0.65122724, -0.51568852, -0.46575384,\n",
       "        -0.46096625, -0.4573046 , -0.45721688, -0.45715629, -0.45715541,\n",
       "        -0.45715476, -0.45715475, -0.45715474, -0.45715474]),\n",
       " 'split1_train_score': array([-0.69314718, -0.63668469, -0.66257062, -0.51156701, -0.46461785,\n",
       "        -0.45956693, -0.45637803, -0.45628259, -0.45623169, -0.45623071,\n",
       "        -0.45623017, -0.45623016, -0.45623015, -0.45623015]),\n",
       " 'split2_train_score': array([-0.69314718, -0.63699848, -0.65224763, -0.50776774, -0.45274452,\n",
       "        -0.4465418 , -0.44189581, -0.44181006, -0.44172794, -0.44172709,\n",
       "        -0.4417262 , -0.44172619, -0.44172618, -0.44172618]),\n",
       " 'split3_train_score': array([-0.69314718, -0.64114688, -0.66982316, -0.5233278 , -0.47635608,\n",
       "        -0.47098325, -0.46748534, -0.46739996, -0.46734228, -0.4673414 ,\n",
       "        -0.46734079, -0.46734078, -0.46734077, -0.46734077]),\n",
       " 'split4_train_score': array([-0.69314718, -0.63710208, -0.66387966, -0.51200138, -0.4634894 ,\n",
       "        -0.45783749, -0.45415721, -0.45406106, -0.45399898, -0.453998  ,\n",
       "        -0.45399734, -0.45399733, -0.45399732, -0.45399732]),\n",
       " 'mean_train_score': array([-0.69314718, -0.63815108, -0.65994966, -0.51407049, -0.46459234,\n",
       "        -0.45917915, -0.4554442 , -0.45535411, -0.45529143, -0.45529052,\n",
       "        -0.45528985, -0.45528984, -0.45528983, -0.45528983]),\n",
       " 'std_train_score': array([0.        , 0.00167367, 0.00714426, 0.00526477, 0.00750097,\n",
       "        0.00779979, 0.0081797 , 0.00817982, 0.00818808, 0.00818808,\n",
       "        0.00818817, 0.00818817, 0.00818817, 0.00818817])}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳得分 0.47002936100154424\n",
      "最佳参数 {'C': 1, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "# 最佳得分和最佳参数\n",
    "print('最佳得分', -grid.best_score_)\n",
    "print('最佳参数', grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'neg-logloss')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cv误差曲线\n",
    "test_means = grid.cv_results_['mean_test_score']\n",
    "test_std = grid.cv_results_['std_test_score']\n",
    "train_means = grid.cv_results_['mean_train_score']\n",
    "train_std = grid.cv_results_['std_train_score']\n",
    "\n",
    "n_cs = len(cs)\n",
    "number_penatlys = len(penaltys)\n",
    "\n",
    "test_scores = np.array(test_means).reshape(n_cs, number_penatlys)\n",
    "train_scores = np.array(train_means).reshape(n_cs, number_penatlys)\n",
    "test_stds = np.array(test_std).reshape(n_cs, number_penatlys)\n",
    "train_stds = np.array(train_std).reshape(n_cs, number_penatlys)\n",
    "\n",
    "x_axis = np.log10(cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    plt.errorbar(x_axis, test_scores[:, i], yerr=test_stds[:, i], label=penaltys[i] + ' Test')\n",
    "    plt.errorbar(x_axis, train_scores[:, i], yerr=train_stds[:, i], label=penaltys[i] + ' Train')\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('log(C)')\n",
    "plt.ylabel('neg-logloss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图给出了L1正则和L2正则下、不同正则参数C对应的模型在训练集上测试集上的正确率（score）。可以看出在训练集上C越大（正则越少）的模型性能越好；但在测试集上当C=1时性能最好（L1正则和L2正则均是）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来看一下选择好的模型在测试集上的表现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集得分 0.5008132160623657\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import log_loss\n",
    "\n",
    "# 这里输出预测为每个标签的概率数组\n",
    "ypp = grid.predict_proba(x_test)\n",
    "test_log_loss = log_loss(y_test, ypp)\n",
    "\n",
    "print('测试集得分', test_log_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试集损失比训练集要大一点，应该是数据量太少，模型不是很完善。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集正确率：  0.7337662337662337\n"
     ]
    }
   ],
   "source": [
    "# 看一下模型在测试集上预测的准确率\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "yp = grid.predict(x_test)\n",
    "\n",
    "accuracy = accuracy_score(y_test, yp)\n",
    "\n",
    "print('测试集正确率： ', accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 线性SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "线性svc最佳得分： -0.4686794966094737\n",
      "线性svc最佳参数： {'C': 0.01}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svc = SVC(kernel='linear', probability=True)\n",
    "\n",
    "# 正则参数数组\n",
    "cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "svc_params = {'C': cs}\n",
    "svc_grid = GridSearchCV(svc, svc_params, cv=5, scoring='neg_log_loss', return_train_score=True)\n",
    "svc_grid.fit(x_train, y_train)\n",
    "\n",
    "print('线性svc最佳得分：', svc_grid.best_score_)\n",
    "print('线性svc最佳参数：', svc_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'neg-logloss')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# svc误差曲线\n",
    "svc_test_means = svc_grid.cv_results_['mean_test_score']\n",
    "svc_test_std = svc_grid.cv_results_['std_test_score']\n",
    "\n",
    "svc_train_means = svc_grid.cv_results_['mean_train_score']\n",
    "svc_train_std = svc_grid.cv_results_['std_train_score']\n",
    "\n",
    "n_cs = len(cs)\n",
    "\n",
    "svc_test_scores = np.array(svc_test_means).reshape(n_cs, 1)\n",
    "svc_train_scores = np.array(svc_train_means).reshape(n_cs, 1)\n",
    "\n",
    "svc_test_stds= np.array(svc_test_std).reshape(n_cs, 1)\n",
    "svc_train_stds= np.array(svc_train_std).reshape(n_cs, 1)\n",
    "\n",
    "x_axis = np.log10(cs)\n",
    "plt.errorbar(x_axis, svc_test_scores, yerr=svc_test_stds, label='Test')\n",
    "plt.errorbar(x_axis, svc_train_scores, yerr=svc_train_stds, label='Train')\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('log(C)')\n",
    "plt.ylabel('neg-logloss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当c=0.01时效果最好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "线性svm在测试集上的正确率： 0.7337662337662337\n"
     ]
    }
   ],
   "source": [
    "# 线性svm模型在测试集上的正确率\n",
    "\n",
    "svc_yp = svc_grid.predict(x_test)\n",
    "\n",
    "svc_accuracy = accuracy_score(y_test, svc_yp)\n",
    "print('线性svm在测试集上的正确率：', svc_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正确率跟logistic回归差不多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RBF核的SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rbf best score  0.46359868808528387\n",
      "rbf best params {'C': 1000, 'gamma': 0.001}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/Swing/.local/lib/python3.7/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'neg_log_loss')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rbf_svc = SVC(kernel='rbf', probability=True)\n",
    "#需要调整的正则参数：\n",
    "#C：     正则系数，C越小，决策边界越平滑 C越大 对样本分错的容忍度越低\n",
    "#gamma： 核函数的宽度\n",
    "#        gamma越小，决策边界越平滑\n",
    "cs = [0.01, 0.1, 1, 10, 100, 1000 ,10000]\n",
    "gammas = [0.0001, 0.001, 0.01, 0.1, 1]\n",
    "rbf_svc_params = {'C': cs, 'gamma': gammas}\n",
    "rbf_svc_grid = GridSearchCV(rbf_svc, param_grid=rbf_svc_params, cv=5, scoring='neg_log_loss', return_train_score=True)\n",
    "rbf_svc_grid.fit(x_train, y_train)\n",
    "\n",
    "print('rbf best score ', -rbf_svc_grid.best_score_)\n",
    "print('rbf best params', rbf_svc_grid.best_params_)\n",
    "\n",
    "rbf_mean_test_score = rbf_svc_grid.cv_results_['mean_test_score']\n",
    "\n",
    "rbf_test_score = np.array(rbf_mean_test_score).reshape(n_cs, len(gammas))\n",
    "\n",
    "x_axis = np.log10(cs)\n",
    "\n",
    "for i, value in enumerate(gammas):\n",
    "    plt.plot(x_axis, rbf_test_score[:, i], label=gammas[i])\n",
    "plt.legend()\n",
    "plt.xlabel('log(C)')\n",
    "plt.ylabel('neg_log_loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rbf核的svm在测试集上的正确率： 0.7142857142857143\n"
     ]
    }
   ],
   "source": [
    "# rbf核的svm模型在测试集上的正确率\n",
    "\n",
    "rbf_yp = rbf_svc_grid.predict(x_test)\n",
    "\n",
    "svc_rbf_accuracy = accuracy_score(y_test, rbf_yp)\n",
    "print('rbf核的svm在测试集上的正确率：', svc_rbf_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 总结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面三个模型在测试集上的正确率来看，rbf核的svm性能要明显比线性svm和logistic回归差一些。\n",
    "究其原因可能是logistic回归和线性svm对线性可分的样本效果更好，而rbf核更适合与线性不可分的情况，且rbf可能会导致过拟合。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
